Projet GMM5 2017
Nathalie Villa-Vialaneix, Gicu Stratan and Soizick Magon de La Giclais
18 janvier, 2018
- Data description
- Microbiote analysis
- Random Forest
- PLS-DA on viruses
- sPLS with regression mode -
ENcondition - sPLS with regression mode -
LBAcondition - Work on one condition
- Session information
This files is a summary of the work made by GMM5 students on data coming from ENVT study.
The data are processed using the following packages:
library(mixOmics)## Warning: package 'mixOmics' was built under R version 3.3.3## Warning: package 'ggplot2' was built under R version 3.3.3library(metagenomeSeq)## Warning: package 'limma' was built under R version 3.3.3## Warning: package 'glmnet' was built under R version 3.3.3library(reshape2)Data description
Data are given in two files included in the directory data :
- the first one is the file
abondances.csvthat contains microbiote data:
df_abundances <- read.delim("../data/abondances.csv", sep = ",",
stringsAsFactors = FALSE)
summary(df_abundances[ ,1:15])## X.blast_taxonomy blast_subject blast_perc_identity
## Length:406 Length:406 Min. : 94.18
## Class :character Class :character 1st Qu.: 99.39
## Mode :character Mode :character Median : 99.80
## Mean : 99.32
## 3rd Qu.:100.00
## Max. :100.00
## blast_perc_query_coverage blast_evalue blast_aln_length
## Min. : 98.13 Min. :0 Min. :430.0
## 1st Qu.:100.00 1st Qu.:0 1st Qu.:466.2
## Median :100.00 Median :0 Median :480.0
## Mean : 99.96 Mean :0 Mean :477.4
## 3rd Qu.:100.00 3rd Qu.:0 3rd Qu.:490.0
## Max. :100.00 Max. :0 Max. :512.0
## seed_id observation_name observation_sum
## Length:406 Length:406 Min. : 224.0
## Class :character Class :character 1st Qu.: 588.5
## Mode :character Mode :character Median : 979.5
## Mean : 9753.7
## 3rd Qu.: 2738.0
## Max. :880523.0
## NG.10214_EN10A_lib136338_4869_1 NG.10214_EN10B_lib136339_4869_1
## Min. : 0.0 Min. : 0.0
## 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 0.0 Median : 0.0
## Mean : 108.4 Mean : 108.4
## 3rd Qu.: 12.0 3rd Qu.: 12.0
## Max. :17044.0 Max. :17663.0
## NG.10214_EN11A_lib136340_4869_1 NG.10214_EN11B_lib136341_4869_1
## Min. : 0.0 Min. : 0.0
## 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 1.0 Median : 1.0
## Mean : 108.4 Mean : 108.4
## 3rd Qu.: 6.0 3rd Qu.: 6.0
## Max. :24223.0 Max. :23739.0
## NG.10214_EN15A_lib136342_4869_1 NG.10214_EN15B_lib136343_4869_1
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 3.00 Median : 4.00
## Mean : 108.37 Mean : 108.37
## 3rd Qu.: 15.75 3rd Qu.: 15.75
## Max. :28421.00 Max. :27946.00The first 9 columns contain information about the different bacteria identified within samples. The following columns contain the two technical replicates (identified by “A” and “B”) of all the farms involved in the study (the farm is identified by a number preceeding the letter “A”/“B”).
Note that some “blast taxonomy” are duplicated:
unique(names(which(table(df_abundances[ ,1]) > 1)))## [1] "Bacteria;Actinobacteria;Actinobacteria;Corynebacteriales;Corynebacteriaceae;Corynebacterium 1;Corynebacterium sp."
## [2] "Bacteria;Actinobacteria;Actinobacteria;Corynebacteriales;Corynebacteriaceae;Corynebacterium 1;unknown species"
## [3] "Bacteria;Actinobacteria;Actinobacteria;Corynebacteriales;Corynebacteriaceae;Corynebacterium;unknown species"
## [4] "Bacteria;Actinobacteria;Actinobacteria;Corynebacteriales;Dietziaceae;Dietzia;Dietzia sp."
## [5] "Bacteria;Actinobacteria;Actinobacteria;Corynebacteriales;Nocardiaceae;Rhodococcus;Rhodococcus sp."
## [6] "Bacteria;Actinobacteria;Actinobacteria;Micrococcales;Brevibacteriaceae;Brevibacterium;Brevibacterium antiquum"
## [7] "Bacteria;Actinobacteria;Actinobacteria;Micrococcales;Brevibacteriaceae;Brevibacterium;unknown species"
## [8] "Bacteria;Actinobacteria;Actinobacteria;Micrococcales;Intrasporangiaceae;Janibacter;unknown species"
## [9] "Bacteria;Actinobacteria;Actinobacteria;Micrococcales;Microbacteriaceae;Leucobacter;unknown species"
## [10] "Bacteria;Actinobacteria;Actinobacteria;Micrococcales;Micrococcaceae;Arthrobacter;Arthrobacter sp."
## [11] "Bacteria;Actinobacteria;Actinobacteria;Streptomycetales;Streptomycetaceae;Streptomyces;Streptomyces sp."
## [12] "Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bacteroides;unknown species"
## [13] "Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Petrimonas;unknown species"
## [14] "Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;unknown species"
## [15] "Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Proteiniphilum;unknown species"
## [16] "Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;unknown species"
## [17] "Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella 1;unknown species"
## [18] "Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella 2;unknown species"
## [19] "Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella 9;unknown species"
## [20] "Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Rikenellaceae;Rikenellaceae RC9 gut group;unknown species"
## [21] "Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Bergeyella;unknown species"
## [22] "Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Flavobacterium;unknown species"
## [23] "Bacteria;Bacteroidetes;Sphingobacteriia;Sphingobacteriales;Sphingobacteriaceae;Olivibacter;unknown species"
## [24] "Bacteria;Bacteroidetes;Sphingobacteriia;Sphingobacteriales;Sphingobacteriaceae;Pedobacter;unknown species"
## [25] "Bacteria;Firmicutes;Bacilli;Bacillales;Planococcaceae;Caryophanon;Caryophanon latum"
## [26] "Bacteria;Firmicutes;Bacilli;Bacillales;Staphylococcaceae;Jeotgalicoccus;unknown species"
## [27] "Bacteria;Firmicutes;Bacilli;Bacillales;Staphylococcaceae;Staphylococcus;Staphylococcus sp."
## [28] "Bacteria;Firmicutes;Bacilli;Lactobacillales;Aerococcaceae;Facklamia;unknown species"
## [29] "Bacteria;Firmicutes;Bacilli;Lactobacillales;Aerococcaceae;unknown genus;unknown species"
## [30] "Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Trichococcus;unknown species"
## [31] "Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;unknown genus;unknown species"
## [32] "Bacteria;Firmicutes;Bacilli;Lactobacillales;Enterococcaceae;Enterococcus;unknown species"
## [33] "Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;Streptococcus pluranimalium"
## [34] "Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;unknown species"
## [35] "Bacteria;Firmicutes;Clostridia;Clostridiales;Clostridiaceae 1;Clostridium sensu stricto 1;unknown species"
## [36] "Bacteria;Firmicutes;Clostridia;Clostridiales;Family XI;Peptoniphilus;unknown species"
## [37] "Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Blautia;unknown species"
## [38] "Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Lachnoclostridium;unknown species"
## [39] "Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Lachnospiraceae NK3A20 group;unknown species"
## [40] "Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae;Intestinibacter;unknown species"
## [41] "Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae;Peptoclostridium;unknown species"
## [42] "Bacteria;Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalibacterium;unknown species"
## [43] "Bacteria;Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Ruminococcaceae UCG-005;unknown species"
## [44] "Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Erysipelothrix;unknown species"
## [45] "Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;unknown genus;unknown species"
## [46] "Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;Hyphomicrobiaceae;Devosia;unknown species"
## [47] "Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;Methylobacteriaceae;Methylobacterium;Methylobacterium sp."
## [48] "Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;Phyllobacteriaceae;Aquamicrobium;unknown species"
## [49] "Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;Rhizobiaceae;Rhizobium;Agrobacterium tumefaciens"
## [50] "Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Alcaligenaceae;Alcaligenes;Alcaligenes faecalis"
## [51] "Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Comamonas;unknown species"
## [52] "Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Oxalobacteraceae;Massilia;unknown species"
## [53] "Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacteriales;Enterobacteriaceae;Escherichia-Shigella;Escherichia coli"
## [54] "Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Mannheimia;Mannheimia sp."
## [55] "Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;Moraxella oblonga"
## [56] "Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;unknown species"
## [57] "Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Pseudomonadaceae;Pseudomonas;Pseudomonas sp."
## [58] "Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Xanthomonadaceae;Luteimonas;unknown species"
## [59] "Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Xanthomonadaceae;Stenotrophomonas;Stenotrophomonas sp."
## [60] "Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Xanthomonadaceae;Stenotrophomonas;unknown species"
## [61] "Bacteria;Tenericutes;Mollicutes;Mycoplasmatales;Mycoplasmataceae;Mycoplasma;Mycoplasma bovirhinis"as well as some of the bast id themselves
unique(names(which(table(df_abundances[ ,2]) > 1)))## [1] "AB680687.1.1434" "AB681778.1.1454" "AF224286.1.1534"
## [4] "AJ491302.1.1509" "AM183097.1.1441" "AY243344.1.1501"
## [7] "AY725811.1.1474" "FJ672272.1.1408" "FJ674571.1.1375"
## [10] "FJ674662.1.1457" "GQ358834.1.1513" "GQ358846.1.1455"
## [13] "HM325988.1.1341" "HM327870.1.1341" "JX986968.1.1511"
## [16] "KC894531.1.1498"Microbiote analysis
Preprocessing and normalization
First, the two technical replicates are merged (simple sums as the counts have already been normalized to identical library sizes)
abundances <- df_abundances[ ,grep("A_", colnames(df_abundances))] +
df_abundances[ ,grep("B_", colnames(df_abundances))]
dim(abundances)## [1] 406 45which leads to 45 samples (columns) in which 406 bacteria have been observed (rows).
Condition (“EN” or “LBA”) is extracted from column names:
condition <- rep("LBA", ncol(abundances))
condition[grep("EN", colnames(abundances))] <- "EN"
condition <- factor(condition)
table(condition)## condition
## EN LBA
## 22 23Also, the farm identifier is extracted from column names:
id_abundances <- as.character(colnames(abundances))
id_abundances <- sapply(id_abundances, function(ac)
substr(ac, nchar(ac) - 19, nchar(ac) - 18))
id_abundances <- gsub("A", "0", id_abundances)
id_abundances <- gsub("N", "0", id_abundances)
table(id_abundances)## id_abundances
## 01 03 04 06 07 09 10 11 15 16 17 18 19 20 21 23 24 25 26 28 29 30 31
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2All but one farm (idenfier: 29) have been sampled twice, once for each condition.
Merging duplicates
One species name is not correct. Let us change it with the right name:
df_abundances[grep("&",df_abundances[ ,1]),1] <- "Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Mannheimia;Mannheimia haemolytica"Duplicated species (based on identical taxonomies) are then merged (counts are summed). To do so, first simplified species names are obtained (last unknown species for every row):
species <- sapply(df_abundances[ ,1], function(aname)
strsplit(aname, ";"))
species <- sapply(species, function(avect) {
find_unknown <- grep("unknown", avect)
if (length(find_unknown) > 0) {
return(avect[-find_unknown])
} else return(avect)
})
species <- unlist(sapply(species, function(avect) avect[length(avect)]))
species <- unname(species)Then, counts corresponding to the same simplified name are summed:
abundances <- apply(abundances, 2, function(acol) tapply(acol, species, sum))and the species names are retrieved:
species <- rownames(abundances)
colnames(abundances) <- paste(condition,id_abundances,sep='_')One species has an unexpected name:
head(species)## [1] "&"
## [2] "[Eubacterium] coprostanoligenes group"
## [3] "[Ruminococcus] gauvreauii group"
## [4] "Acetobacteraceae bacterium SAP1007.2"
## [5] "Acholeplasma laidlawii PG-8A"
## [6] "Achromobacter sp."that corresponds to the row number in the original data (checked also directly in the file sent by Elias on 10/09).
Exploratory analysis: distribution of one sample
The effect of different normalization is first explored by analyzing the distributions of the counts in the first sample before and after normalization. Distribution before normalization is provided as:
df <- data.frame(abundances)
names(df) <- paste0("sample", 1:ncol(df))
ggplot(df, aes(x = sample1 +1)) + geom_histogram(bins = 50) + scale_y_log10() +
theme_bw(base_size = 18) + xlab("counts (sample 1)") + ggtitle("Sample 1 distribution in raw data")## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 41 rows containing missing values (geom_bar).
and with a log-transformation by
df <- as.data.frame(log(abundances+1))
names(df) <- paste0("sample", 1:ncol(df))
ggplot(df, aes(x = sample1 + 1)) + geom_histogram(bins = 50) +
theme_bw(base_size = 18) + xlab("counts (sample 1)") +
ggtitle("Sample 1 distribution (log)")
It is commun in metagenomic datasets to perform TSS (Total Sum Scaling) before further normalization. TSS transformation computes relative abundances: \[ y_{ij} = \frac{n_{ij}}{\sum_{k=1}^p n_{ik}} \] for \(n_{ij}\) the counts of species \(j\) in sample \(i\), \(p\) the number of species and \(n\) the number of individuals.
abundances_TSS <- apply(abundances, 1, function(asample) asample / sum(asample))
df <- as.data.frame(t(abundances_TSS))
names(df) <- paste0("sample", 1:ncol(df))
dim(df)## [1] 270 45ggplot(df, aes(x = sample1 + 1)) + geom_histogram(bins = 50) +
theme_bw(base_size = 18) + xlab("relative abundance (sample 1)") + scale_x_log10() + ggtitle("Sample 1 distribution (TSS)")
The next two histograms are based on the normalized counts with:
- CLR (Centered Log Ratio) transformation: \[ \tilde{y}_{ij} = \log \frac{y_{ij}}{\sqrt[p]{\prod_{k=1}^p y_{ik}}}. \]
abundances_CLR <- logratio.transfo(abundances_TSS, logratio = "CLR",
offset = 1)
class(abundances_CLR) <- "matrix"
df <- data.frame(t(abundances_CLR))
names(df) <- paste0("sample", 1:ncol(df))
dim(df)## [1] 270 45ggplot(df, aes(x = sample1)) + geom_histogram(bins = 50) + theme_bw(base_size = 18) +
xlab("counts (sample 1)") + ggtitle("Sample 1 distribution (CLR)")
- ILR (Isometric Log Ratio) transformation \[ \tilde{\mathbf{Y}}' = \tilde{\mathbf{Y}} \times \mathbf{V} \] for \(\tilde{\mathbf{Y}}\) the matrix of CLR transformed data and a given matrix \(\mathbf{V}\) with \(p\) rows and \(p-1\) columns such that \(\mathbf{V} \mathbf{V}^\top = \mathbb{I}_{p-1}\) and \(\mathbf{V}^\top \mathbf{V} = \mathbb{I} + a \mathbf{1}\), \(a\) being any positive number and \(\mathbf{1}\) a vector full of 1.
abundances_ILR <- logratio.transfo(abundances_TSS, logratio = "ILR",
offset = 1)
class(abundances_ILR) <- "matrix"
df <- data.frame(t(abundances_ILR))
names(df) <- paste0("sample", 1:ncol(df))
dim(df)## [1] 269 45ggplot(df, aes(x = sample1)) + geom_histogram(bins = 50) +
theme_bw(base_size = 18) + xlab("counts (sample 1)") +
ggtitle("Sample 1 distribution (ILR)")
- CSS transformation, which is an adaptative extension for metagenomic data of the quantile normalization used in microarray expression datasets. It is designed so as to account for technical differences between samples.
abundances_CSS <- newMRexperiment(abundances)
abundances_CSS <- cumNorm(abundances_CSS)## Default value being used.df <- data.frame(MRcounts(abundances_CSS))
names(df) <- paste0("sample", 1:ncol(df))
dim(df)## [1] 270 45ggplot(df, aes(x = sample1 + 1)) + geom_histogram(bins = 50) +
theme_bw(base_size = 18) + xlab("counts (sample 1)") +
scale_y_log10() + scale_x_log10()+ ggtitle("Sample 1 distribution (CSS)")## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 16 rows containing missing values (geom_bar).
The less asymetric distribution seems to be the one obtained with the CLR transformation and the log-transformed CSS.
Exploratory analysis: distribution of all samples
Distributions of all samples according to the type of transformation and the sample is provided below:
df_log <- log10(abundances + 1)
df_log <- data.frame(df_log)
rownames(df_log) <- NULL
names(df_log) <- paste0("Sample", 1:ncol(df_log))
df_log <- melt(df_log)
df_CLR <- data.frame(t(abundances_CLR))
names(df_CLR) <- paste0("Sample", 1:ncol(df_CLR))
df_CLR <- melt(df_CLR)
df_ILR <- data.frame(t(abundances_ILR))
names(df_ILR) <- paste0("Sample", 1:ncol(df_ILR))
df_ILR <- melt(df_ILR)
df_CSS <- data.frame(log(MRcounts(abundances_CSS)) + 1)
names(df_CSS) <- paste0("Sample", 1:ncol(df_CSS))
df_CSS <- melt(df_CSS)
all_sizes <- c(nrow(df_log), nrow(df_CLR), nrow(df_ILR), nrow(df_CSS))
df <- data.frame(rbind(df_log, df_CLR, df_ILR, df_CSS),
"type" = rep(c("log", "CLR", "ILR", "log-CSS"), all_sizes))
ggplot(df, aes(x = variable, y = value)) + geom_boxplot() + theme_bw(base_size = 18) +
facet_wrap(~ type, scales = "free_y") + xlab("samples") +
theme(axis.text.x = element_blank())## Warning: Removed 6381 rows containing non-finite values (stat_boxplot).
Exploratory analysis: PCA
Log
A first exploratory analysis is performed with PCA on (merged) raw counts with log transformation:
pca_raw <- pca(log(t(abundances) + 1), ncomp = ncol(abundances),
logratio = 'none')
plot(pca_raw)
that shows a good percentage of explained variance for the first axis.
Projection of the individuals on the first two PCs also shows a good separation between the two conditions:
plotIndiv(pca_raw,
comp = c(1,2),
pch = 16,
ind.names = FALSE,
group = condition,
col.per.group = color.mixo(1:2),
legend = TRUE,
title="PCA for log-tranformed data",
size.title = rel(2.5),
size.ylabel = rel(1.5),
size.xlabel = rel(1.5),
size.legend.title = rel(1.5),cex=4)
TSS+CLR
The same analysis is used with TSS normalized counts subsequently transformed by CLR or ILR (which is the expected analysis):
pca_CLR <- pca(abundances_TSS + 1, ncomp = nrow(abundances_TSS),
logratio = 'CLR')
plot(pca_CLR)
plotIndiv(pca_CLR,
comp = c(1,2),
pch = 16,
ind.names = FALSE,
group = condition,
col.per.group = color.mixo(1:2),
legend = TRUE,
title="PCA for CLR-tranformed data")
TSS+ILR
pca_ILR <- pca(abundances_TSS + 1, ncomp = nrow(abundances_TSS) - 1,
logratio = 'ILR')
plot(pca_ILR)
plotIndiv(pca_ILR,
comp = c(1,2),
pch = 16,
ind.names = FALSE,
group = condition,
col.per.group = color.mixo(1:2),
legend = TRUE,
title="PCA for ILR-tranformed data")
CSS
log_CSS <- log(MRcounts(abundances_CSS) + 1)
pca_CSS <- pca(t(log_CSS), ncomp = ncol(log_CSS))
plot(pca_CSS)
plotIndiv(pca_CSS,
comp = c(1,2),
pch = 16,
ind.names = FALSE,
group = condition,
col.per.group = color.mixo(1:2),
legend = TRUE,
title="PCA for CSS-tranformed data")
Differences between the two types of samples
A first PLS-DA is computed (with 10-fold CV) to check the efficiency of the method and which type of distance to use in its computation.
with log transformation
set.seed(11)
res_plsda <- plsda(log(t(abundances)+1), condition, ncomp = nlevels(condition))
res_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)
plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA Comp 1 - 2')
PLS-DA shows a good separation between the two groups and indicates that the Mahalanobis distance provides the lower overall classification error.
Then, sparse PLS-DA is used (with the multilevel approach) to check which number of components to select.
Creation of data set log transformed and without the calf 29:
clean_log <- data.frame(log(t(abundances[ ,id_abundances != "29"]) + 1))
names(clean_log) <- species
clean_condition <- factor(condition[id_abundances != "29"])
clean_id <- factor(id_abundances[id_abundances != "29"])set.seed(33)
res_plsda <- tune.splsda(clean_log, clean_condition,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
test.keepX = 1:20, validation = 'Mfold', folds = 10,
dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = FALSE)## Splitting the variation for 1 level factor.plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepX## comp1 comp2
## 5 14Finally sparse PLS-DA is performed and the variables explaining the two types of samples are obtained:
res_splsda <- splsda(clean_log, clean_condition,
ncomp = nlevels(clean_condition), multilevel = clean_id,
keepX = sel_keepX)## Splitting the variation for 1 level factor.plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA Comp 1 - 2')
head(selectVar(res_splsda, comp = 1)$name)## [1] "Mycoplasma bovoculi M165/69" "Corynebacterium camporealensis"
## [3] "Moraxella" "Aquamicrobium"
## [5] "Peptoclostridium"plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)
# cim(res_splsda,comp=1,row.names = paste(clean_id,clean_condition,sep='_'),title="Heatmap with splsda values")
# test<-clean_log[,which(species%in%selectVar(res_splsda, comp = 1)$name)]
# cim(sapply(test,as.numeric),row.names = paste(clean_id,clean_condition,sep='_'),title="Heatmap with clean_log values")EN vs LBA: Student test on selected Variables
selected <- selectVar(res_splsda, comp = 1)$name
for (ind in seq_along(selected)) {
df <- data.frame(counts = abundances[selected[ind], ],
condition = condition)
p <- ggplot(df, aes(x = condition, y = counts + 1, fill = condition)) +
geom_boxplot() + theme_bw() + scale_y_log10() +
scale_fill_manual(values = c("dodgerblue2","darkorange")) +
ggtitle(selected[ind]) + ylab(expression(log[10] ~ "(count + 1)"))
print(p)
}
all_pvals <- sapply(seq_along(selected), function(ind) {
var_en <- clean_log[clean_condition == "EN",selected[ind]]
var_lba <- clean_log[clean_condition == "LBA",selected[ind]]
pval <- t.test(var_en, var_lba, paired = TRUE)$p.value
})
res <- data.frame(bacteria = selected, pvalue = all_pvals,
FWER = p.adjust(all_pvals, method = "BH"))
res## bacteria pvalue FWER
## 1 Mycoplasma bovoculi M165/69 3.728266e-12 1.553412e-11
## 2 Corynebacterium camporealensis 6.213648e-12 1.553412e-11
## 3 Moraxella 2.367632e-11 3.946053e-11
## 4 Aquamicrobium 4.077402e-11 4.484280e-11
## 5 Peptoclostridium 4.484280e-11 4.484280e-11All tests are found positive: those bacteria are found differentially present in EN/LBA samples.
with TSS+CLR transformation
abundances_CLR <- data.frame(abundances_CLR)
names(abundances_CLR) <- species
set.seed(11)
res_plsda <- plsda(abundances_CLR, condition, ncomp = nlevels(condition))
res_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)
plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (CLR)')clean_CLR <- data.frame(abundances_CLR[id_abundances != "29", ])
names(clean_CLR) <- species
set.seed(33)
res_plsda <- tune.splsda(clean_CLR, clean_condition,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = FALSE)
plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepXres_splsda <- splsda(clean_CLR, clean_condition,
ncomp = nlevels(clean_condition), multilevel = clean_id,
keepX = sel_keepX)
plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (CLR)')plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max')
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max')with CSS transformation
abundances_CSS <- data.frame(t(log_CSS))
names(abundances_CSS) <- species
set.seed(11)
res_plsda <- plsda(abundances_CSS, condition, ncomp = nlevels(condition))
res_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)
plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (CSS)')
clean_CSS <- data.frame(abundances_CSS[id_abundances != "29", ])
names(clean_CSS) <- species
set.seed(33)
res_plsda <- tune.splsda(clean_CSS, clean_condition,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = FALSE)## Splitting the variation for 1 level factor.plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepX## comp1 comp2
## 5 14res_splsda <- splsda(clean_CSS, clean_condition,
ncomp = nlevels(clean_condition), multilevel = clean_id,
keepX = sel_keepX)## Splitting the variation for 1 level factor.plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (CSS)')
plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max')
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max')
with log transformation (but not paired)
set.seed(33)
res_plsda <- tune.splsda(log(t(abundances)+1), condition,
ncomp = nlevels(condition),
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = FALSE)
plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepX## comp1 comp2
## 20 15res_splsda <- splsda(log(t(abundances)+1), condition,
ncomp = nlevels(condition), keepX = sel_keepX)
plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (log)')
plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)
with TSS+CLR transformation (not paired)
set.seed(11)
res_plsda <- plsda(abundances_CLR, condition, ncomp = nlevels(condition))
res_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)
plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (CLR)')set.seed(33)
res_plsda <- tune.splsda(abundances_CLR, condition, ncomp = nlevels(condition),
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = F)
plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepXres_splsda <- splsda(abundances_CLR, condition, ncomp = nlevels(condition),
keepX = sel_keepX)
plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (CLR)')plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)Random Forest
In this section will be performed Random Forest algorithm for classification, with a virus as response vector and all bacteria as explanatory variables in the model.
library(randomForest,quietly=T)Data preparation:
df_pathogenes<-read.delim("../data/pathogenes.csv",sep = ",")
pathogenes<-df_pathogenes[,-c(1,9)]
pathogenes<-as.data.frame(ifelse(pathogenes=='p',1,0))
pathogenes<-as.data.frame(apply(t(pathogenes),1,as.factor))
id_pathogenes<-df_pathogenes[,1]
id_pathogenes <- gsub("-","0",id_pathogenes)
id_pathogenes <- gsub(" ","",id_pathogenes)
id_pathogenes <- sapply(as.character(id_pathogenes),FUN=function(x){substr(x,nchar(x)-1,nchar(x))})
id_pathogenes <- id_pathogenes[id_pathogenes!="29"]
id_pathogenes <- paste0(id_pathogenes,"_",rev(condition[id_abundances!="29"]))The list of calf names is not in the same order in the pathogenes file as in the abundances file. The abundances file order will be kept:
id_abundances_bis <- paste0(id_abundances[-grep("29",id_abundances)],"_",
condition[-grep("29",id_abundances)])
pathogenes <- pathogenes[df_pathogenes[,1]!="ICSA-29",]
Y<-pathogenes[match(id_abundances_bis,id_pathogenes),]
rownames(Y)<-id_abundances_bisY contains the seven response vectors.
cat(" 0 1\n\n")## 0 1for (virus in colnames(Y)){
cat(virus,"\n",table(Y[,virus]),fill=T)
}## Ct.RSV
## 37 7
## Ct.PI.3
## 41 3
## Ct.Coronavirus
## 22 22
## Ct.P.multocida
## 6 38
## Ct.M.haemolytica
## 31 13
## Ct.M.bovis
## 39 5
## Ct.H.somni
## 26 18The bacteria names are too long for the plots, they will be renamed:
X <- clean_log
X <- as.data.frame(sapply(X,as.integer))
colnames(X)<- make.names(names(X))Random Forest on both conditions
Random Forest on RSV:
fit <- randomForest(Y$Ct.RSV ~ ., data = X,classwt=table(Y$Ct.RSV),ntree=3000)
fit$confusion## 0 1 class.error
## 0 36 1 0.02702703
## 1 7 0 1.00000000varImpPlot(fit)
round(fit$importance[order(fit$importance[, 1], decreasing = TRUE), ][1:5],3)
plot(fit$err.rate[, 1], type = "l", xlab = "number of trees", ylab = "error OOB")cat(fit$predicted)Random Forest on PI.3:
fit <- randomForest(Y$Ct.PI.3 ~ ., data = X,classwt=table(Y$Ct.PI.3),ntree=1000,mtry=10)
fit$confusion## 0 1 class.error
## 0 41 0 0
## 1 3 0 1Nothing in class 1…
varImpPlot(fit)
round(fit$importance[order(fit$importance[, 1], decreasing = TRUE), ][1:5],3)
plot(fit$err.rate[, 1], type = "l", xlab = "number of trees", ylab = "error OOB")Random Forest on P.multocida:
fit <- randomForest(Y$Ct.P.multocida ~ ., data = X,classwt=table(Y$Ct.P.multocida),ntree=1000,mtry=16)
fit$confusion## 0 1 class.error
## 0 0 6 1
## 1 0 38 0Nothing in class 0
varImpPlot(fit)
fit$importance[order(fit$importance[, 1], decreasing = TRUE), ][1:5]
plot(fit$err.rate[, 1], type = "l", xlab = "nombre d'arbres", ylab = "erreur OOB")Random Forest on M.haemolytica:
fit <- randomForest(Y$Ct.M.haemolytica ~ ., data = X,classwt=table(Y$Ct.M.haemolytica),ntree=1000,mtry=40)
fit$confusion## 0 1 class.error
## 0 31 0 0
## 1 13 0 1varImpPlot(fit)
fit$importance[order(fit$importance[, 1], decreasing = TRUE), ][1:5]
plot(fit$err.rate[, 1], type = "l", xlab = "number of trees", ylab = "error OOB")Random Forest on M.bovis:
fit <- randomForest(Y$Ct.M.bovis ~ ., data = X,classwt=table(Y$Ct.M.bovis) , ntree=500,mtry=40)
fit$confusion## 0 1 class.error
## 0 39 0 0
## 1 5 0 1varImpPlot(fit)
fit$importance[order(fit$importance[, 1], decreasing = TRUE), ][1:5]
plot(fit$err.rate[, 1], type = "l", xlab = "number of trees", ylab = "error OOB")Random Forest on H.somni:
fit <- randomForest(Y$Ct.H.somni ~ ., data = X,classwt=table(Y$Ct.H.somni),ntree=1000,mtry=16)
fit$confusion## 0 1 class.error
## 0 23 3 0.1153846
## 1 7 11 0.3888889varImpPlot(fit)
fit$importance[order(fit$importance[, 1], decreasing = TRUE), ][1:5]
plot(fit$err.rate[, 1], type = "l", xlab = "number of trees", ylab = "error OOB")Random Forest on EN condition
Study only on EN condition samples
X_EN<-X[grep("EN",colnames(abundances)),]
X_LBA<-X[grep("LBA",rownames(Y)),]
Y_EN<-Y[grep("EN",colnames(abundances)),]
Y_LBA<-Y[grep("LBA",rownames(Y)),]
clean_log_EN <- clean_log[grep("EN",rownames(clean_log)),]
clean_id_EN <- clean_id[grep("EN",names(clean_id))]
clean_log_LBA <- clean_log[grep("LBA",rownames(clean_log)),]
clean_id_LBA <- clean_id[grep("LBA",names(clean_id))]Random Forest on RSV - EN condition:
fit <- randomForest(Y_EN$Ct.RSV ~ ., data = X_EN,classwt=table(Y_EN$Ct.H.somni), ntree=5000)
fit$confusion## 0 1 class.error
## 0 21 0 0
## 1 0 0 NaNRandom Forest on PI.3 - EN condition:
fit <- randomForest(Y_EN$Ct.PI.3 ~ ., data = X_EN,classwt=table(Y_EN$Ct.H.somni), ntree=5000)
fit$confusion## 0 1 class.error
## 0 20 0 0
## 1 2 0 1Random Forest on M.haemolytica - EN condition:
fit <- randomForest(Y_EN$Ct.M.haemolytica~ ., data = X_EN,classwt=table(Y_EN$Ct.H.somni), ntree=5000)
fit$confusion## 0 1 class.error
## 0 12 2 0.1428571
## 1 8 0 1.0000000Random Forest on M.bovis - EN condition:
fit <- randomForest(Y_EN$Ct.M.bovis~ ., data = X_EN,classwt=table(Y_EN$Ct.H.somni), ntree=5000)
fit$confusion## 0 1 class.error
## 0 20 0 0
## 1 2 0 1Random Forest on P.multocida - EN condition:
fit <- randomForest(Y_EN$Ct.P.multocida ~ ., data = X_EN,classwt=table(Y_EN$Ct.H.somni), ntree=5000)
fit$confusion## 0 1 class.error
## 0 0 2 1
## 1 0 20 0Random Forest on H.somni - EN condition:
fit <- randomForest(Y_EN$Ct.H.somni ~ ., data = X_EN,classwt=table(Y_EN$Ct.H.somni), ntree=5000)
fit$confusion## 0 1 class.error
## 0 12 1 0.07692308
## 1 3 6 0.33333333PLS-DA on viruses
PLS-DA on RSV
set.seed(11)
res_plsda <- plsda(X=clean_log,Y=Y$Ct.RSV, ncomp = nlevels(clean_condition))
res_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)
plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (RSV)')
set.seed(33)
res_plsda <- tune.splsda(X=clean_log,Y=Y$Ct.RSV,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = F)## Splitting the variation for 1 level factor.plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepX## comp1 comp2
## 13 14res_splsda <- splsda(X=clean_log,Y=Y$Ct.RSV,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
keepX = sel_keepX)## Splitting the variation for 1 level factor.plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (RSV)')
test <- predict(res_splsda,clean_log)
pred <- test$class$mahalanobis.dist[,2]
get.confusion_matrix(Y$Ct.RSV,predicted=pred)## predicted.as.0 predicted.as.1
## 0 36 1
## 1 6 1head(selectVar(res_splsda, comp = 1)$name)## [1] "Lactococcus lactis" "Saccharibacteria"
## [3] "Moheibacter" "Streptococcus gallolyticus"
## [5] "Facklamia" "Flavobacterium"plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)
PLS-DA on PI.3
set.seed(11)
res_plsda <- plsda(X=clean_log,Y=Y$Ct.PI.3, ncomp = nlevels(clean_condition))
res_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)## Warning in MCVfold.splsda(X, Y, multilevel = multilevel, validation = validation, : At least one class is not represented in one fold, which may unbalance the error rate.
## Consider a number of folds lower than the minimum in table(Y): 3
## Warning in MCVfold.splsda(X, Y, multilevel = multilevel, validation = validation, : At least one class is not represented in one fold, which may unbalance the error rate.
## Consider a number of folds lower than the minimum in table(Y): 3plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (PI.3)')
set.seed(33)
res_plsda <- tune.splsda(X=clean_log,Y=Y$Ct.PI.3,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = F)
plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepXERROR: Splitting the variation for 1 level factor. The SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not convergeThe SGCCA algorithm did not converge
Error in colnames<-(*tmp*, value = c(“0”, “1”)) : length of ‘dimnames’ [2] not equal to array extent
res_splsda <- splsda(X=clean_log,Y=Y$Ct.PI.3,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
keepX = sel_keepX)
plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (PI.3)')head(selectVar(res_splsda, comp = 1)$name)
plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)PLS-DA on Coronavirus
set.seed(11)
res_plsda <- plsda(X=clean_log,Y=Y$Ct.Coronavirus, ncomp = nlevels(clean_condition))
res_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)
plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (Coronavirus)')
set.seed(33)
res_plsda <- tune.splsda(X=clean_log,Y=Y$Ct.Coronavirus,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = F)## Splitting the variation for 1 level factor.plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepX## comp1 comp2
## 13 14res_splsda <- splsda(X=clean_log,Y=Y$Ct.Coronavirus,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
keepX = sel_keepX)## Splitting the variation for 1 level factor.plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (Coronavirus)')
Si on limite le nombre de variables, on n’arrive pas a discrimner de maniere lineaire la presence ou l’absence du Coronavirus.
head(selectVar(res_splsda, comp = 1)$name)## [1] "Alcaligenes sp." "Moraxella lacunata"
## [3] "Bacteroides" "Prevotella 7"
## [5] "Streptococcus gallolyticus" "Mycoplasma dispar"plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)
PLS-DA on P.multocida
set.seed(11)
res_plsda <- plsda(X=clean_log,Y=Y$Ct.P.multocida, ncomp = nlevels(clean_condition))
res_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)
plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (P.multocida)')
set.seed(33)
res_plsda <- tune.splsda(X=clean_log,Y=Y$Ct.P.multocida,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = F)## Splitting the variation for 1 level factor.plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepX## comp1 comp2
## 1 16res_splsda <- splsda(X=clean_log,Y=Y$Ct.P.multocida,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
keepX = sel_keepX)## Splitting the variation for 1 level factor.plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (P.multocida)')
head(selectVar(res_splsda, comp = 1)$name)## [1] "Saccharomonospora glauca K62"plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)
PLS-DA on M.haemolytica
set.seed(11)
res_plsda <- plsda(X=clean_log,Y=Y$Ct.M.haemolytica, ncomp = nlevels(clean_condition))
res_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)
plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (M.haemolytica)')
set.seed(33)
res_plsda <- tune.splsda(X=clean_log,Y=Y$Ct.M.haemolytica,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = F)## Splitting the variation for 1 level factor.plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepX## comp1 comp2
## 18 13res_splsda <- splsda(X=clean_log,Y=Y$Ct.M.haemolytica,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
keepX = sel_keepX)## Splitting the variation for 1 level factor.plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (M.haemolytica)')
head(selectVar(res_splsda, comp = 1)$name)## [1] "Eubacterium rectale" "Propionibacteriaceae"
## [3] "Bacteroidales S24-7 group" "Bacillus sp."
## [5] "[Ruminococcus] gauvreauii group" "Prevotellaceae"plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)
PLS-DA on M.bovis
set.seed(11)
res_plsda <- plsda(X=clean_log,Y=Y$Ct.M.bovis, ncomp = nlevels(clean_condition))
res_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)## Warning in MCVfold.splsda(X, Y, multilevel = multilevel, validation = validation, : At least one class is not represented in one fold, which may unbalance the error rate.
## Consider a number of folds lower than the minimum in table(Y): 4
## Warning in MCVfold.splsda(X, Y, multilevel = multilevel, validation = validation, : At least one class is not represented in one fold, which may unbalance the error rate.
## Consider a number of folds lower than the minimum in table(Y): 4plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (M.bovis)')
set.seed(33)
res_plsda <- tune.splsda(X=clean_log,Y=Y$Ct.M.bovis,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = F)## Splitting the variation for 1 level factor.plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepX## comp1 comp2
## 12 7res_splsda <- splsda(X=clean_log,Y=Y$Ct.M.bovis,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
keepX = sel_keepX)## Splitting the variation for 1 level factor.plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (M.bovis)')
head(selectVar(res_splsda, comp = 1)$name)## [1] "Mycoplasma bovis" "Eubacteriaceae bacterium BL-152"
## [3] "Atopobium fossor" "Filifactor alocis ATCC 35896"
## [5] "Bacteroides pyogenes" "Bergeyella"plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)
PLS-DA on H.somni
set.seed(11)
res_plsda <- plsda(X=clean_log,Y=Y$Ct.H.somni, ncomp = nlevels(clean_condition))
res_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)
plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (H.somni)')
set.seed(33)
res_plsda <- tune.splsda(X=clean_log,Y=Y$Ct.H.somni,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = F)## Splitting the variation for 1 level factor.plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepX## comp1 comp2
## 2 5res_splsda <- splsda(X=clean_log,Y=Y$Ct.H.somni,
ncomp = nlevels(clean_condition),
multilevel = clean_id,
keepX = sel_keepX)## Splitting the variation for 1 level factor.plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (H.somni)')
head(selectVar(res_splsda, comp = 1)$name)## [1] "Streptococcus" "Actinoalloteichus cyanogriseus"plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)
sPLS with regression mode - EN condition
sel_samples <- which(condition == "EN")
set.seed(1205)
spls <- spls(X = log(t(abundances)+1)[sel_samples, ],
sapply(pathogenes[sel_samples, ], as.numeric) - 1, ncomp = 2,
mode = "regression", keepX = c(10,10), keepY = c(7,7))## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nulleplotIndiv(spls, comp = 1:2, rep.space= 'XY-variate',
ind.names = id_abundances[sel_samples], legend = F,
title = 'sPLS comp 1 - 2, XY-space',size.title = rel(2))
pls <- pls(as.matrix(sapply(clean_log_EN,as.numeric)), as.matrix(sapply(Y_EN,as.numeric)), ncomp = 2, mode = "regression")## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nullespls <- spls(as.matrix(sapply(clean_log_EN,as.numeric)), as.matrix(sapply(Y_EN,as.numeric)), ncomp =2,
keepX= c(15,15), keepY=c(7,7), mode = "regression")## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nulletune.pls <- perf(pls, validation = "Mfold", folds = 20,
progressBar = FALSE, nrepeat = 10)## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nulletune.spls <- perf(spls, validation = "Mfold", folds = 20,
progressBar = FALSE, nrepeat = 10)## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nulletune.pls$Q2.total## Q2.total
## 1 comp -0.0595258
## 2 comp -0.1119991plot(tune.pls$Q2.total)
abline(h=0.0975)
Sample Plots
plotIndiv(spls, comp = 1:2, rep.space= 'Y-variate', ind.names = clean_id[grep("EN",names(clean_id))],
legend = TRUE, title = 'sPLS comp 1 - 2, Y-space')
plotIndiv(spls, comp = 1:2, rep.space= 'X-variate', ind.names = clean_id[grep("EN",names(clean_id))],
legend = TRUE, title = 'sPLS comp 1 - 2, X-space')
plotIndiv(spls, comp = 1:2, rep.space= 'XY-variate', ind.names = clean_id[grep("EN",names(clean_id))],
legend = TRUE, title = 'sPLS comp 1 - 2, XY-space')
Individual plots can be displayed on three different subspaces spanned either by the X variable, the Y variable or the mean subspace in which coordinates are averaged from the first two subspaces (XY).
Variable Plot
plotVar(spls, comp =1:2, var.names = list(X.label = species,
Y.label = TRUE), cex = c(4, 5))
sPLS with regression mode - LBA condition
pls <- pls(as.matrix(sapply(clean_log_LBA,as.numeric)), as.matrix(sapply(Y_LBA,as.numeric)), ncomp = 8, mode = "regression")## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nullespls <- spls(as.matrix(sapply(clean_log_LBA,as.numeric)), as.matrix(sapply(Y_LBA,as.numeric)), ncomp =8,
keepX= c(15,15), keepY=c(7,7), mode = "regression")## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nulletune.pls <- perf(pls, validation = "Mfold", folds = 20,
progressBar = FALSE, nrepeat = 10)## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nulletune.spls <- perf(spls, validation = "Mfold", folds = 20,
progressBar = FALSE, nrepeat = 10)## Warning: The SGCCA algorithm did not converge## Warning: The SGCCA algorithm did not converge
## Warning: The SGCCA algorithm did not converge
## Warning: The SGCCA algorithm did not converge## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nulletune.pls$Q2.total## Q2.total
## 1 comp -0.020106011
## 2 comp -0.019756234
## 3 comp -0.022894423
## 4 comp -0.001669778
## 5 comp 0.033861515
## 6 comp -0.046381700
## 7 comp 0.105589909
## 8 comp -0.012843887plot(tune.pls$Q2.total)
abline(h=0.0975)
Sample Plots
plotIndiv(spls, comp = 1:2, rep.space= 'Y-variate', ind.names = clean_id_LBA,
legend = F, title = 'sPLS comp 1 - 2, Y-space')
plotIndiv(spls, comp = 1:2, rep.space= 'X-variate', ind.names = clean_id_LBA,
legend = F, title = 'sPLS comp 1 - 2, X-space')
plotIndiv(spls, comp = 1:2, rep.space= 'XY-variate', ind.names = clean_id_LBA,
legend = F, title = 'sPLS comp 1 - 2, XY-space')
Individual plots can be displayed on three different subspaces spanned either by the X variable, the Y variable or the mean subspace in which coordinates are averaged from the first two subspaces (XY).
Variable Plot
plotVar(spls, comp =1:2, var.names = list(X.label = species,
Y.label = TRUE), cex = c(4, 5))
cim(spls, comp = 1, xlab = "virus", ylab = "genes")
selectVar(spls, comp = 1)$X$name## [1] "Paracoccus solventivorans" "Caryophanon latum"
## [3] "Histophilus somni" "Acholeplasma laidlawii PG-8A"
## [5] "Arcobacter" "Lutibacterium sp."
## [7] "[Ruminococcus] gauvreauii group" "Lachnoclostridium"
## [9] "Staphylococcus xylosus" "Proteiniclasticum"
## [11] "Tetrasphaera" "Janibacter"
## [13] "Corynebacterium confusum" "Weeksella"
## [15] "Leucobacter"test <- cbind(Y,clean_log[,which(species%in%selectVar(spls, comp = 1)$X$name)])
cim(as.matrix(sapply(clean_log[,which(species%in%selectVar(spls, comp = 1)$X$name)],as.numeric)))
Work on one condition
Regroupment of RSV, PI.3, Coronavirus
group_virus<-sapply(Y,as.numeric)
group_virus<-group_virus-1
group_virus<-group_virus[,1]+group_virus[,2]+group_virus[,3]
group_virus[group_virus>0]<-1
group_virus <- as.factor(group_virus)
group_virus_EN <- group_virus[grep("EN",rownames(Y))]
group_virus_LBA <- group_virus[grep("LBA",rownames(Y))]PLS-DA on group_virus - EN condition
set.seed(11)
res_plsda <- plsda(X=clean_log_EN,Y=group_virus_EN, ncomp = nlevels(group_virus_EN))## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nulleres_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)
plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (RSV,PI.3 et Coronavirus) - EN')
set.seed(33)
res_plsda <- tune.splsda(X=clean_log_EN,Y=group_virus_EN,
ncomp = nlevels(group_virus_EN),
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = F)## Warning in MCVfold.splsda(X, Y, multilevel = multilevel, validation = validation, : At least one class is not represented in one fold, which may unbalance the error rate.
## Consider a number of folds lower than the minimum in table(Y): 9
## Warning in MCVfold.splsda(X, Y, multilevel = multilevel, validation = validation, : At least one class is not represented in one fold, which may unbalance the error rate.
## Consider a number of folds lower than the minimum in table(Y): 9plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepX## comp1 comp2
## 18 12res_splsda <- splsda(X=clean_log_EN,Y=group_virus_EN,
ncomp = nlevels(group_virus_EN),
keepX = sel_keepX)## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nulleplotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (RSV,PI.3 and Coronavirus) - EN')
test <- predict(res_splsda,clean_log_EN)
pred <- test$class$mahalanobis.dist[,2]
get.confusion_matrix(group_virus_EN,predicted=pred)## predicted.as.0 predicted.as.1
## 0 9 0
## 1 0 13head(selectVar(res_splsda, comp = 1)$name)## [1] "Leucobacter" "Corynebacterium confusum"
## [3] "Pseudochrobactrum" "Gelidibacter"
## [5] "Streptococcus gallolyticus" "Moraxella lacunata"plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)
cim(res_splsda,comp=1)
test<-clean_log_EN[,which(species%in%selectVar(res_splsda, comp = 1)$name)]
cim(sapply(test,as.numeric),row.names = clean_id_EN)Presence vs Absence of viruses: Student tests
selected <- selectVar(res_splsda, comp = 1)$name
for (ind in seq_along(selected)) {
df <- data.frame(counts = clean_log_EN[,selected[ind]],
condition = group_virus_EN)
p <- ggplot(df, aes(x = condition, y = counts + 1, fill = group_virus_EN)) +
geom_boxplot() + theme_bw() + scale_y_log10() +
scale_fill_manual(values = c("dodgerblue2","darkorange")) +
ggtitle(selected[ind]) + ylab(expression(log[10] ~ "(count + 1)"))
print(p)
}
\(H_0\): true difference in means is equal to 0.
\(H_1\): true difference in means is not equal to 0.
all_pvals <- sapply(seq_along(selected), function(ind) {
var_0 <- clean_log_EN[group_virus_EN == 0,selected[ind]]
var_1 <- clean_log_EN[group_virus_EN == 1,selected[ind]]
pval <- t.test(var_0, var_1)$p.value
})
res <- data.frame(bacteria = selected, pvalue = all_pvals,
FWER = p.adjust(all_pvals, method = "BH"))
res## bacteria pvalue FWER
## 1 Leucobacter 0.03481991 0.07834479
## 2 Corynebacterium confusum 0.03045623 0.07834479
## 3 Pseudochrobactrum 0.03477661 0.07834479
## 4 Gelidibacter 0.01618017 0.07834479
## 5 Streptococcus gallolyticus 0.02026699 0.07834479
## 6 Moraxella lacunata 0.01911170 0.07834479
## 7 Gracilibacteria 0.02123296 0.07834479
## 8 Ochrobactrum sp. 0.07461385 0.11865154
## 9 Arthrobacter sp. 0.09038226 0.11865154
## 10 Trueperella pyogenes 0.04204057 0.08408115
## 11 Faecalicoccus pleomorphus 0.02917639 0.07834479
## 12 Phyllobacteriaceae 0.08753570 0.11865154
## 13 Propionibacteriaceae 0.15041696 0.15926502
## 14 Helcococcus ovis 0.13733066 0.15926502
## 15 Sporosarcina sp. 0.05414143 0.09745458
## 16 Olivibacter 0.09228453 0.11865154
## 17 Chryseobacterium sp. 0.14258569 0.15926502
## 18 Delftia sp. 0.16064938 0.16064938#cim(res_splsda, row.sideColors = color.mixo(group_virus_EN),row.names = clean_id_EN)PLS-DA on group_virus - LBA condition
set.seed(11)
res_plsda <- plsda(X=clean_log_LBA,Y=group_virus_LBA, ncomp = nlevels(group_virus_LBA))## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nulleres_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)
plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (RSV,PI.3 et Coronavirus) - LBA')
set.seed(33)
res_plsda <- tune.splsda(X=clean_log_LBA,Y=group_virus_LBA,
ncomp = nlevels(group_virus_LBA),
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = F)## Warning in MCVfold.splsda(X, Y, multilevel = multilevel, validation = validation, : At least one class is not represented in one fold, which may unbalance the error rate.
## Consider a number of folds lower than the minimum in table(Y): 8
## Warning in MCVfold.splsda(X, Y, multilevel = multilevel, validation = validation, : At least one class is not represented in one fold, which may unbalance the error rate.
## Consider a number of folds lower than the minimum in table(Y): 8## Warning: The SGCCA algorithm did not converge
## Warning: The SGCCA algorithm did not converge
## Warning: The SGCCA algorithm did not converge
## Warning: The SGCCA algorithm did not converge
## Warning: The SGCCA algorithm did not converge
## Warning: The SGCCA algorithm did not converge
## Warning: The SGCCA algorithm did not convergeplot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepX## comp1 comp2
## 17 11res_splsda <- splsda(X=clean_log_LBA,Y=group_virus_LBA,
ncomp = nlevels(group_virus_LBA),
keepX = sel_keepX)## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nulleplotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (RSV,PI.3 et Coronavirus) - LBA')
test <- predict(res_splsda,clean_log_LBA)
pred <- test$class$mahalanobis.dist[,2]
get.confusion_matrix(group_virus_LBA,predicted=pred)## predicted.as.0 predicted.as.1
## 0 7 1
## 1 0 14table(group_virus_LBA)## group_virus_LBA
## 0 1
## 8 14head(selectVar(res_splsda, comp = 1)$name)## [1] "Paracoccus alcaliphilus" "Psychrobacter pulmonis"
## [3] "Brachybacterium sp." "Leptotrichiaceae"
## [5] "Jeotgalicoccus psychrophilus" "Delftia sp."plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)
Presence vs Absence of viruses: Student tests
selected <- selectVar(res_splsda, comp = 1)$name
for (ind in seq_along(selected)) {
df <- data.frame(counts = clean_log_LBA[,selected[ind]],
condition = group_virus_LBA)
p <- ggplot(df, aes(x = condition, y = counts + 1, fill = group_virus_LBA)) +
geom_boxplot() + theme_bw() + scale_y_log10() +
scale_fill_manual(values = c("dodgerblue2","darkorange")) +
ggtitle(selected[ind]) + ylab(expression(log[10] ~ "(count + 1)"))
print(p)
}
\(H_0\): true difference in means is equal to 0.
\(H_1\): true difference in means is not equal to 0.
all_pvals <- sapply(seq_along(selected), function(ind) {
var_0 <- clean_log_LBA[group_virus_LBA == 0,selected[ind]]
var_1 <- clean_log_LBA[group_virus_LBA == 1,selected[ind]]
pval <- t.test(var_0, var_1)$p.value
})
res <- data.frame(bacteria = selected, pvalue = all_pvals,
FWER = p.adjust(all_pvals, method = "BH"))
res## bacteria pvalue FWER
## 1 Paracoccus alcaliphilus 0.057147872 0.1826383
## 2 Psychrobacter pulmonis 0.049883006 0.1826383
## 3 Brachybacterium sp. 0.088959771 0.1826383
## 4 Leptotrichiaceae 0.008310064 0.1412711
## 5 Jeotgalicoccus psychrophilus 0.103865601 0.1826383
## 6 Delftia sp. 0.070899958 0.1826383
## 7 Comamonas 0.110969363 0.1826383
## 8 Mycoplasma bovis 0.139664562 0.1826383
## 9 Curtobacterium flaccumfaciens 0.024006091 0.1826383
## 10 Helcococcus ovis 0.136882674 0.1826383
## 11 Staphylococcus arlettae CVD059 0.171795776 0.1957109
## 12 Ruminococcaceae UCG-005 0.172686096 0.1957109
## 13 Desemzia incerta 0.136521146 0.1826383
## 14 Thauera 0.187778889 0.1970221
## 15 Solibacillus 0.197022072 0.1970221
## 16 Peptoniphilus 0.108754858 0.1826383
## 17 Methylobacterium sp. 0.054201536 0.1826383#cim(res_splsda, row.sideColors = color.mixo(group_virus_LBA),row.names = clean_id_LBA)PLS-DA on PI.3 - EN condition
set.seed(11)
res_plsda <- plsda(X=clean_log_EN,Y=Y_EN$Ct.PI.3, ncomp = nlevels(Y_EN$Ct.PI.3))## Warning in cor(A[[k]], variates.A[[k]]): l'écart type est nulleres_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)## Warning in MCVfold.splsda(X, Y, multilevel = multilevel, validation = validation, : At least one class is not represented in one fold, which may unbalance the error rate.
## Consider a number of folds lower than the minimum in table(Y): 2## Warning in MCVfold.splsda(X, Y, multilevel = multilevel, validation = validation, : At least one class is not represented in one fold, which may unbalance the error rate.
## Consider a number of folds lower than the minimum in table(Y): 2plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (PI.3) - EN')
set.seed(33)
res_plsda <- tune.splsda(X=clean_log_EN,Y=Y_EN$Ct.PI.3,
ncomp = nlevels(Y_EN$Ct.PI.3),
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = F)
plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepXError: At least one class is not represented in one fold, which may unbalance the error rate.
res_splsda <- splsda(X=clean_log_EN,Y=Y_EN$Ct.PI.3,
ncomp = nlevels(Y_EN$Ct.PI.3),
keepX = sel_keepX)
plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (PI.3) - EN')head(selectVar(res_splsda, comp = 1)$name)
plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)PLS-DA on PI.3 - LBA condition
set.seed(11)
res_plsda <- plsda(X=clean_log_LBA,Y=Y_LBA$Ct.PI.3, ncomp = nlevels(Y_LBA$Ct.PI.3))
res_perf <- perf(res_plsda, validation = 'Mfold', folds = 5,
progressBar = FALSE, nrepeat = 20)
plot(res_perf, overlay = 'measure', sd = TRUE)
plotIndiv(res_plsda , comp = c(1, 2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'PLS-DA (PI.3) - LBA')Error: there is unbalanced class of 1
set.seed(33)
res_plsda <- tune.splsda(X=clean_log_LBA,Y=Y_LBA$Ct.PI.3,
ncomp = nlevels(Y_LBA$Ct.PI.3),
test.keepX = 1:20, validation = 'Mfold',
folds = 10, dist = 'mahalanobis.dist', nrepeat = 10,
progressBar = F)
plot(res_plsda)
sel_keepX <- res_plsda$choice.keepX[1:2]
sel_keepXError: there is unbalanced class of 1
res_splsda <- splsda(X=clean_log_LBA,Y=Y_LBA$Ct.PI.3,
ncomp = nlevels(Y_LBA$Ct.PI.3),
keepX = sel_keepX)
plotIndiv(res_splsda, comp = c(1,2), ind.names = FALSE, ellipse = TRUE,
legend = TRUE, title = 'sPLS-DA (PI.3) - LBA')head(selectVar(res_splsda, comp = 1)$name)
plotLoadings(res_splsda, comp = 1, method = 'mean', contrib = 'max',
size.title = 1)
plotLoadings(res_splsda, comp = 2, method = 'mean', contrib = 'max',
size.title = 1)Random Forest on group_virus - EN condition:
fit <- randomForest(group_virus_EN ~ ., data = X_EN,classwt=table(group_virus_EN), ntree=5000)
fit$confusion## 0 1 class.error
## 0 1 8 0.88888889
## 1 1 12 0.07692308Random Forest on group_virus - LBA condition:
fit <- randomForest(group_virus_LBA ~ ., data = X_LBA,classwt=table(group_virus_LBA), ntree=5000)
fit$confusion## 0 1 class.error
## 0 0 8 1.0000000
## 1 2 12 0.1428571Session information
sessionInfo()## R version 3.3.2 (2016-10-31)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 16299)
##
## locale:
## [1] LC_COLLATE=French_France.1252 LC_CTYPE=French_France.1252
## [3] LC_MONETARY=French_France.1252 LC_NUMERIC=C
## [5] LC_TIME=French_France.1252
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] randomForest_4.6-12 reshape2_1.4.2 metagenomeSeq_1.16.0
## [4] RColorBrewer_1.1-2 glmnet_2.0-13 foreach_1.4.3
## [7] Matrix_1.2-7.1 limma_3.30.13 Biobase_2.34.0
## [10] BiocGenerics_0.20.0 mixOmics_6.3.0 ggplot2_2.2.1
## [13] lattice_0.20-34 MASS_7.3-45
##
## loaded via a namespace (and not attached):
## [1] gtools_3.5.0 colorspace_1.3-2 htmltools_0.3.5
## [4] yaml_2.1.14 DBI_0.5-1 matrixStats_0.52.2
## [7] plyr_1.8.4 stringr_1.2.0 munsell_0.4.3
## [10] gtable_0.2.0 htmlwidgets_0.8 caTools_1.17.1
## [13] codetools_0.2-15 evaluate_0.10.1 labeling_0.3
## [16] knitr_1.15.1 httpuv_1.3.3 rARPACK_0.11-0
## [19] Rcpp_0.12.9 KernSmooth_2.23-15 xtable_1.8-2
## [22] corpcor_1.6.9 scales_0.4.1 backports_1.0.5
## [25] gdata_2.17.0 jsonlite_1.5 mime_0.5
## [28] RSpectra_0.12-0 gplots_3.0.1 gridExtra_2.2.1
## [31] ellipse_0.3-8 digest_0.6.12 stringi_1.1.2
## [34] dplyr_0.5.0 shiny_1.0.0 grid_3.3.2
## [37] rprojroot_1.2 tools_3.3.2 bitops_1.0-6
## [40] magrittr_1.5 rgl_0.97.0 lazyeval_0.2.0
## [43] tibble_1.2 tidyr_0.6.1 pkgconfig_2.0.1
## [46] assertthat_0.1 rmarkdown_1.3 iterators_1.0.8
## [49] R6_2.2.0 igraph_1.1.2